Who Health Systems Hire as a Healthcare AI Governance and Risk Officer

What Does a Healthcare AI Governance and Risk Officer Do Before the Sepsis Alert Fails?

A vendor's sepsis alert has been live in two of your hospitals for five months. A nurse manager mentions that it fires constantly on the oncology floor. Nobody can produce the validation packet, the retraining date, or how the model performed on patients over 75. The Healthcare AI Governance and Risk Officer is the person hired so that answer exists before the question arrives.

The seat runs three things most systems currently run nowhere. An inventory of every model touching a patient encounter, including the ones a service line bought on a departmental card. An intake gate no vendor gets past without evidence. A monitoring program that watches performance after go-live, when the population shifts and the EHR upgrades and nobody tells the vendor. Around those sit the artifacts other people read later: committee minutes, validation packets, and an incident log a malpractice carrier will go through line by line.

One healthcare IT recruiting desk, the same one that supplies the pay band further down, describes the VP-level version as running the AI governance committee, holding the model inventory, approving procurement of anything that touches a patient encounter, and sitting on the board's risk committee ¹. That is a recruiter's composite of the searches it has run rather than a survey of health systems, so read the scope as a shape and check it against your own org chart. Notice what is absent. This person does not manage the ML engineering organization and does not ship models; that stays with the VP of AI Engineering or the CTO ¹. The authority here is the ability to stop a deployment, and it works only when it comes from outside the team that wants the deployment.

Titles move around more than the work does. Clinical AI Governance Lead, VP of AI Risk, Clinical Quality AI Specialist: a posting under that last title describes leading governance, validation and monitoring of AI and LLM tools ³. Search on responsibilities rather than title, or the shortlist will be four people. Regulatory footing is public and worth reading first: ONC's HTI-1 certification program publishes its decision support intervention and predictive model materials openly ⁴, and the specific obligations that attach to a given deployment are a question for counsel and for your compliance officer, not for a job description.

Which Backgrounds Produce a Credible Clinical AI Risk Owner?

Four feeders produce most credible candidates: clinical informatics, hospital quality and patient safety, regulatory affairs from a device or diagnostics company, and validation-side data science. Each supplies half the job and misses the other half, so the strongest hires have already crossed once, deliberately, and can tell you what the crossing taught them.

The clinical informaticist reads a study design and survives a room of physicians who think governance is a tax. What they often lack is contract literacy and comfort with subgroup statistics. The quality and patient safety leader is the most underrated feeder on the list: root cause analysis, failure mode review, event reporting thresholds and survey readiness are the closest existing discipline to model incident review, and a person who has run a serious safety event through a system knows how organizations behave when something goes wrong. The regulatory affairs veteran from software as a medical device arrives knowing what an evidence dossier is and, more usefully, how to write a refusal that holds up when a vendor escalates it.

The unexpected feeder is banking. Model risk management under the Federal Reserve and OCC guidance has spent fifteen years doing structurally the same job: an inventory, risk tiering, independent validation separate from model development, and annual review. Those candidates arrive fluent in the machinery a health system is trying to build and need roughly a year of clinical translation. Pharmacovigilance and clinical trial biostatistics produce a similar shape.

What none of these feeders produces is adversarial technical depth. That is a different hire, closer to an AI red team engineer, and the governance officer's job is to commission that testing and read the result rather than to run it.

How Do You Tell a Real Model Interrogator From a Performed One?

Ask for a decision that cost someone something. A candidate who has actually held this seat has blocked a deployment a chief wanted, and can give you the evidence gap, the alternative they offered, and what happened over the following six months. Performed expertise produces frameworks and maturity models. Real expertise produces a specific Tuesday and a name.

A second tell: ask what they would demand from a vendor before an ambient documentation tool goes live. A weak answer requests the model card. A strong one names artifacts and the reason for each: the training population and its dates, performance broken out by the subgroups your service area actually contains, whether the validation was local or the vendor's own, the retraining cadence, what happens when the EHR version changes underneath the model, and how the vendor found out about their last failure. The reasons matter more than the list, because the list is now easy to generate and the reasons are not.

Third, ask about a model they approved that later went wrong. Anyone with three years in this work has one. If the answer is that nothing has gone wrong, either the program is new or the person was not the owner.

The practice behind the skill is worth probing directly, because this person's own work now runs through assistants. The good ones draft the vendor questionnaire with a model and then verify every regulatory citation it produced, having learned that a confident wrong citation to an agency or a state statute is the most expensive thing a model hands you. A habit worth hearing about: asking the model to argue the vendor's side, then finding the point where that argument rests on something unverifiable. And ask what they refuse to delegate. The answer you want is the risk tier and the go or no-go decision, kept by hand, in writing, with a name on it. Assessing that kind of judgment is exactly the problem a hiring team faces when the work product itself is co-written, which is the same difficulty covered in hiring a talent acquisition specialist fluent in AI work.

What Does the Clinical AI Governance Seat Pay, and Where Do You Source One?

As of mid-2026, a healthcare IT recruiting desk benchmarks the VP-level AI governance and risk officer at $240K to $390K base, with total compensation including bonus and equity between $320K and $560K ¹. No federal wage series exists for the title yet, and nothing independent corroborates that band, so treat it as one desk's asking price rather than a market average.

The defensible internal number comes from a proxy you already own: price this seat against your own senior director or VP band for Quality, Compliance or Risk, because that is the peer group whose authority the job has to match if a refusal is going to survive a service line chief. The recruiting benchmark tells you what the outside market is asking. Your own grade structure tells you what the refusal will be worth.

Demand for the function is broader than healthcare. McKinsey's State of AI reporting puts the share of organizations that hired AI compliance specialists in the past twelve months at 13 percent ², which is small in absolute terms and large relative to a job category that barely existed. Expect to compete with banks and insurers for the model risk candidates.

Sourcing runs through professional communities rather than job boards. AMIA, HIMSS and the informatics track at your regional health information exchange hold the clinical informatics population. The American Society for Quality and patient safety organizations hold the second feeder. For the banking crossover, look at people who have already moved once into health plans or payer analytics. Academic medical centers with standing AI governance committees are the most productive feeder employers, because they built these programs early and have people two years into the practice, and the same is true of the larger integrated systems that stood up model validation groups before anyone required it. Vendor-side clinical affairs teams contain a subset who are visibly tired of being on the sales side of these conversations.

Close the Offer on Authority, Not on Title

The offer is lost on reporting line more often than on money. A candidate worth hiring will ask who they report to, whether they can halt a go-live without a countersignature, and what happened the last time someone in the organization said no to a service line chief. Vague answers on those three read as a decorative seat, and the strong candidates have already turned one of those down.

What they care about, in rough order: a reporting line outside the group that deploys models, standing membership on the committee that approves procurement, a budget line for external validation, and one or two analysts so the inventory is not maintained by hand at night. Also access to clinicians. This job dies quietly when its holder becomes a document producer nobody consults.

What kills the offer: discovering during the process that four models are already live and undocumented while the job description describes building a program from scratch. Say that out loud early. The candidates you want find inherited mess motivating and dishonesty about it disqualifying.

On location, the policy half of the work travels and the credibility half does not. Committee work, incident review, and the conversations that make a refusal survivable happen in person, so most systems run this hybrid out of the corporate campus with regular travel to sites. A fully remote arrangement is workable at multi-state systems that already run governance across time zones, and it costs something real in the first year, which is exactly when this person is building the relationships that let them say no later. Structuring the interview around a work sample rather than a philosophy discussion is the same problem faced when hiring an S and OP planner into a role defined by judgment calls.

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